Trending November 2023 # Want To Become A Data Manager? Here Is All You Need To Know! # Suggested December 2023 # Top 14 Popular

You are reading the article Want To Become A Data Manager? Here Is All You Need To Know! updated in November 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Want To Become A Data Manager? Here Is All You Need To Know!

In-detail Data manager roles, jobs, colleges, and courses

Roles and responsibilities:

The data manager analyzes the data needs of the business or a company. Data manager needs to research and evaluate sources to determine any kinds of limitations. He/She should be capable of applying sampling techniques to effectively comparing and analyzing the data. The data manager also needs to prepare detailed reports for management and other purposes. His tasks also include training the assistant, designing computer codes using various languages, referring to previous instances and findings to come up with an ideal method of gathering data and results. The manager also defines and utilizes statistical methods to solve problems such as economics and engineering. 

Average salary (pPer annum)

: US$64891


Bachelor’s degree in mathematics, statistics, computer science, and or any related field. 

Strong command over math and analytical skills

Ability to complete milestones and work at the right time

A multitasker for effectively managing customer service and interpersonal relationships

Able to organize and analyze statistical information and findings.

Top 3 Online Courses: 

Knowledge Management and Big Data in Business (edX):

The course is provided by edX along with The Hong Kong Polytechnic University. This is to understand the role of knowledge management practitioners in creating business value. It teaches how to analyze large quantities of data and information through analytics and how to manage it. It makes the learner understand how the use of cloud services to drive new values and business models. 

Enterprise Data Management (edX):

The course teaches how to design relational databases that can be used to manage operational systems, how to query relational databases using a structured query language (SQL), how to design data warehouse and intelligence system, and understand the principles of data profiling, data integration, and master data management. 

Master Data Management (udemy):

 The course equips the learner with an overview of enterprise and master data. This helps in understanding master data management. Also busts the myths and realities about MDM by building blocks and components layer models of MDM. The course has 9 sessions for a better understanding of data management and its components. 

Top 3 educationalinstitutionsInstitutes Offering the Program with degree: 

Bachelor’s in Computer Science along with Data Science and Economics: Massachusetts Institute of Technology.   

Bachelor’s in Data Science: ESSEC and CentraleSupelec.   

Bachelor’s and MastersMaster’s in Data Science: IE Spain

Top 5 Recruiters for This Job:


Capgemini is a consulting, technology services, and digital transformation company with data used for defining the end of the database, and then formulate the organizational data strategy, including standards of data quality, the flow of data within the organization, and security of the data.  

Fractal Analytics:

The company has emerged as one of the top analytics services providers in the country.  The company has a global footprint boasting of several Fortune 500 companies from industries like retail, insurance, and technology. It has many branches in all parts of India hiring new positions. 


Amazon is one of the biggest e-commerce companies around the world and is also among the top data recruiters worldwide. Amazon is a giant of data and to comprehend the intensity of data it requires data engineers for its core operations from marketing streamlining, logistics, and inventory management to sales expectation and even HR analytics.

You're reading Want To Become A Data Manager? Here Is All You Need To Know!

Is Digital Marketing A Good Career: All You Need To Know

blog / Digital Marketing Is Digital Marketing a Good Career Option? Here’s What You Need to Know

Share link

We cannot imagine a world without the internet today – on both professional as well as personal fronts. If you are someone who is looking to build a career in the digital space, digital marketing is perhaps the most popular option you’ve come across. According to the CMO Survey, marketers are predicted to allocate 57% of the total budget to digital marketing activities and are likely to increase their spending by 16% in 2023. As a beginner, it is common to wonder, ‘is digital marketing a good career choice?’ Well, if you are enticed by a fast-paced working environment that holds the power to transform a business through engagement, digital marketing is a prospective career path for you. Let’s take a closer look at what this career path entails and why it is here to stay. 

What is Digital Marketing? What are Some Popular Digital Marketing Methods?

A well-rounded digital marketing strategy comprises a combination of tools and techniques that form a successful marketing campaign. Given the vast scope of digital marketing today, there are many different ways to engage with customers and build brand awareness. Here are a few popular digital marketing methods: 

Social Media Marketing

Search Engine Optimization (SEO) 

Email Marketing 

Content Marketing 

Search Engine Marketing 

Affiliate Advertising 

Google Analytics 

In-App Marketing

Ecommerce Advertising 

Let us address the question of the hour, ‘is digital marketing a good career’ by looking at some popular careers in the field.

What are Some Popular Careers in Digital Marketing?

Digital Marketing Manager

Social Media Marketer

SEO or SEM Specialist

User Experience (UX) Designer

Email Marketing Specialist 

Virtual Reality (VR) Developer 

Content Strategist

Artificial Intelligence (AI) Specialist

Now that we have looked at in-demand job roles in the digital marketing industry, let’s take a brief look at the salary scale of digital marketers. 

What is the Average Salary for a Digital Marketer?

For an insight into the average pay scale based on job role, check out our detailed blog post on the Best Salary Range a Digital Marketer Can Expect. 

Career Outlook for Digital Marketers

Digital marketing ranks among the top three hottest skills that Americans are learning in 2023. As per a recent CMO survey, digital marketing activities have become a key investment for marketers today. They are known to lead digital transformations in 73% of companies. In fact, 77.4% of businesses are now investing in website optimization and 69% in digital media.

The future looks bright for digital marketers as increasing competition among data-driven businesses contributes to a growing demand for skilled professionals in the field. 

Is There a High Demand for Digital Marketing Jobs?

There are nearly five billion internet users across the world as of April 2023. Since over 63% of the global population widely uses the internet, there is no doubt why there is a growing demand for skilled digital marketers. In fact, according to a recent LinkedIn report, digital marketing is among the top ten most in-demand skills that employers are looking for. 

There is a rising need for marketing professionals with the right hard skills to fulfill specific digital marketing roles. For example, skills like ad serving and marketing analytics have grown by 84.6% and 46.1%, respectively. 

Since there are so many facets to digital marketing and an apparent digital skills gap, now is a great time to kickstart a career in the field. 

Tips for Getting Started in Digital Marketing

Digital marketing is a lucrative career path that is constantly evolving, which can be intimidating for beginners. We have put together seven tips to help you break into the field with ease. 

Enroll in a

digital marketing course 

Choose a specialization

Earn a Google Ads certification

Master Google Analytics

Become an SEO expert 

Understand marketing and data analytics

If you are a marketing professional who is creative and enjoys a fast-paced work environment, a digital marketing career might just be your calling. Building a career in such a highly competitive field demands you to be well-prepared with the right skill set. Take your preparation to the next level with online digital marketing courses at Emeritus and embark on a successful digital marketing career! 

By Neha Menon

Write to us at [email protected]

Google Clips: All You Need To Know

Google introduced several new products alongside the launch of the Pixel 2 and Pixel 2 XL smartphones last year. One of the new products that the company announced is the Google Clips. Every year, during the Google I/O conference, the company announces something new and interesting.

Most of these new products are based on a new idea and are different from the rest. Google Clips is a new idea that people may or may not like. Remember the Google Glass? This is not exactly similar though as you don’t wear it on your head, and given the AI’s rise, this new Google product is AI-powered, too.

What is Google Clips?

Alright, so let’s get right to it. Google Clips is a small portable camera that will automatically take photos to capture moments as and when they happen. It can even take short video clips of your close ones, friends or even your dog. You attach the Clip on to your body and activate it, but a better way to sue it would be to place it somewhere where it has the view of the area where your kids play, or whatever it is that you want to it to capture.

Everything happens with the help of the machine-learning AI system on-board. You don’t have to take photos, instead, you can just place it a convenient place, or even wear it if you don’t find that creepy, and whenever Clips finds interesting moments, it will automatically start to capture photos and videos of you, your family, and even pets within that area of view.

You can import the recordings as either stills, videos, or GIFs. It comes in a tough package as well, allowing the users to use it outdoors. It has an IPX4 water resistance certificate as well, that should protect it from water splash, but not when it is submerged in water. It’s only a bit of protection from water, so take care. You can connect the Clips’ to your Android or iOS smartphone via Bluetooth or WiFi. Also, Pixel users get free storage for their Google Clips photos and videos!

Google Clips is priced at $249!

Google Clips compatibility

Google Clips, sadly is not compatible with many, let alone all, Android phones and tablets yet. In fact, only a few devices are a go for Google Clips, and that includes the obvious ones in Google Pixel and Pixel 2, Samsung Galaxy S7 and S8, while on the iOS side, the iPhone 8 and iPhone 8 Plus.

Google Pixel

Google Pixel 2

Samsung Galaxy S7

Samsung Galaxy S8

iPhone 8

iPhone 8 Plus

Google Clips Specs

The Clips features a single 12-megapixel camera with a f/2.4 aperture, a 1.55µm pixel size, and a 130-degree field of view. The camera also has auto-focus, a nighttime mode, and can record videos at 15 FPS.

It doesn’t record sound though, as there is no microphone. It comes with 16GB of internal storage and a battery that can last up to 3 hours on the continuous shooting mode.

Reasons to buy it

The second good enough reason is the software of course. Using the camera is simple, as you just need to twist the lens. Then you can either place it on a spot, or wear it on your, and forget it’s even there. Most people will not forget though, as the camera isn’t very cheap. So, whenever some activity appears in the drawing room, where you have put up Google Clips, the device will automatically capture it — we all know it’s not always possible to pick out our smartphone because those moments cannot be anticipated, created, or repeated.

If you’re a parent or someone who loves to travel, then this is a good option for you. Instead of mounting on a GoPro and taking pictures yourself, the Google Clips will do it for you without your interference. Parents can take endless photos of their kids, which they love doing, without missing the action themselves. Usually, when you point the camera at kids, they tend to stop what they’re doing and pose, but that won’t happen with Clips’!

Adventure goers can also use clips to capture moments on the go, without having to stop and take photos. However, the first case scenario is better suited for Clips, as it will associate with faces much more easily than new places.

So there you go, there are some really good reasons to buy the Google Clips camera, especially if you’re a parent! And over time, the A.I will get better and Google may allow it to recognize unfamiliar faces as well and it could act as a security camera. The potential with A.I is huge and that’s what makes this camera very interesting.

How Google Clips works

The software will look at things around you and your life. It will then familiarise itself with the faces, people, things that you always see. It will remember your actions, the things you like to do, and much more. The face recognition software runs on the camera itself, which makes things faster.

As and when it thinks a memory is worthy of being saved, the Clips’ camera will take a burst of shots at 15FPS. You can then use the entire set of photos as a GIF, or you can select your favorite still photo from the bunch.

You get an accompanying app that you install on your smartphone, from where you can edit and select which pictures need to be uploaded to the cloud. The best part is that all the photos captured by the Clips’ camera are stored offline within the device. It’s not uploaded to Google servers, or anywhere else that its own storage, which is pretty secure. And makes it less creepy.

The Google Clips camera also has a shutter button though, tapping on which takes a shot clip. A light indicates when it is taking pictures, but it’s very subtle. Google actually says that it is best to just leave the camera somewhere. The clip case provided with the camera acts as a stand or a clip.

Is it worth the $249 price?

This is the big question, isn’t it? Should you spend $250 on a camera that’s not better than a standard GoPro, and doesn’t let you do much. But then it’s all about not doing much, and still save moments that are automatically identified and captured by the AI. So, it really depends on you.

If you’re someone who likes to live in the moment and not behind the lens, and at the same time don’t care much about image quality, then this is for you. Yes, the price is on the higher side, and the specs aren’t that great either, but that doesn’t matter — it is not your price-for-specs thing, it’s rather a price-for-moments thing. But you do get a different experience with Google Clips, unlike any other camera.

For others, the Clips’ is just a waste of money, so don’t bother purchasing it if you’re all about professional looking photos and videos. There are many cheaper cameras that can clip on to your shirt or jeans and record videos.

When does Google Clips release?

Google has already released Clips’ and it is available for purchase through the Google Store. However, as of now, the Clips is out of stock and we have no idea when it will return. Previously, when it was briefly available, the delivery dates ranged up to March 2023.

So, yes, if you’re interested in purchasing the Google Clips’ camera, then you will have to wait a little longer. All you can do right now is join the waitlist there!

So, what do you think about Google Clips? At what price Google Clips would have made a nice purchase for you?

Motorola Moto P30: All You Need To Know

Many smartphone vendors have been accused of blatantly copying designs from Apple and the most recent high profile company to do so was Huawei and the P20 series. A look at the P20 and P20 Pro is a mirror of what the Apple iPhone X looks like and to make things even more interesting (or not) is the newly announced Motorola Moto P30, a smartphone that is basically a copy of a copy. That makes sense, right?

After a handful of leaks that also involved the company mistakenly publishing details of the Motorola Moto P30 on its official website ahead of time, the phone has finally been launched in China. Like we had seen in the leaks, the P30 is indeed an iPhone X rip-off, even going further to mimic the wallpaper used on the iPhone X press images. However, on the inside, it’s completely something else, as seen below.

Related: The best Android phones without the notch

Moto P30 specs

6.2-inch 19:9 FHD+ LCD display

Qualcomm Snapdragon 636 processor


64GB or 128GB expandable storage

Dual 16MP + 5MP main camera

12MP front camera

3000mAh battery

Android 8.0 Oreo

Extras: Bluetooth 5.0, USB-C, 3.5mm audio jack, rear-mounted scanner, 18W fast charging, etc.

The Moto P30 ships with a notched display screen, with the front camera among the sensors that are housed by the cutout, and an iPhone X-like vertical dual-lens camera on the back alongside a fingerprint scanner. Even more interesting is that the official marketing images have wallpapers that resemble what we’ve seen before on the iPhone X.

Related: The best Motorola phones to buy right now

Still, Motorola can claim it’s following in the lines of the Lenovo Z5 handset that went live in China back in June, but then again, we’d still be talking about a copy of a copy. On the inside, there’s the powerful midrange chipset, Qualcomm Snapdragon 636, mated with a massive 6GB RAM and two storage options of 64GB and 128GB with room to expand via a microSD card.

Keeping the Moto P30 alive is a 3000mAh battery unit that is charged via a USB-C port and to ensure you don’t have to wait for ages before you get back on the road, there’s support for 18W fast charging technology. As for software, there’s Android 8.0 Oreo running the show out of the box and since Motorola is owned by Lenovo, the P30 has the latter’s ZUI skin on top of Oreo.

Moto P30 price and availability

Prior to its launch, there wasn’t much talk about the Motorola Moto P30, but the phone is now among us. However, this initial release is targeting the Chinese market, meaning this variant has no support for Google apps and services. Still, we expect that the Moto P30 will find its way to the rest of the world soon, probably via the IFA 2023 event in Berlin this September.

Related: Android Pie release roadmap: When will your device get the update?

In China, the Moto P30 has a price tag of CNY 1,999 (about $290) for the base model and CNY 2,099 for the superior variant with 128GB storage. The P30 can be had in three color variants of Bright Black, Ice White, and Aurora Blue, which draws even more inspiration from the Huawei P20 Pro.

Here’s All You Need To Know About Encoding Categorical Data (With Python Code)


Understand what is Categorical Data Encoding

Learn different encoding techniques and when to use them


The performance of a machine learning model not only depends on the model and the hyperparameters but also on how we process and feed different types of variables to the model. Since most machine learning models only accept numerical variables, preprocessing the categorical variables becomes a necessary step. We need to convert these categorical variables to numbers such that the model is able to understand and extract valuable information.

A typical data scientist spends 70 – 80% of his time cleaning and preparing the data. And converting categorical data is an unavoidable activity. It not only elevates the model quality but also helps in better feature engineering. Now the question is, how do we proceed? Which categorical data encoding method should we use?

In this article, I will be explaining various types of categorical data encoding methods with implementation in Python.

In case you want to learn concepts of data science in video format, check out our course- Introduction to Data Science

What is categorical data?

Since we are going to be working on categorical variables in this article, here is a quick refresher on the same with a couple of examples. Categorical variables are usually represented as ‘strings’ or ‘categories’ and are finite in number. Here are a few examples:

The city where a person lives: Delhi, Mumbai, Ahmedabad, Bangalore, etc.

The department a person works in: Finance, Human resources, IT, Production.

The highest degree a person has: High school, Diploma, Bachelors, Masters, PhD.

The grades of a student:  A+, A, B+, B, B- etc.

In the above examples, the variables only have definite possible values. Further, we can see there are two kinds of categorical data-

Ordinal Data: The categories have an inherent order

Nominal Data: The categories do not have an inherent order

In Ordinal data, while encoding, one should retain the information regarding the order in which the category is provided. Like in the above example the highest degree a person possesses, gives vital information about his qualification. The degree is an important feature to decide whether a person is suitable for a post or not.

While encoding Nominal data, we have to consider the presence or absence of a feature. In such a case, no notion of order is present. For example, the city a person lives in. For the data, it is important to retain where a person lives. Here, We do not have any order or sequence. It is equal if a person lives in Delhi or Bangalore.

For encoding categorical data, we have a python package category_encoders. The following code helps you install easily.

pip install category_encoders Label Encoding or Ordinal Encoding

We use this categorical data encoding technique when the categorical feature is ordinal. In this case, retaining the order is important. Hence encoding should reflect the sequence.

In Label encoding, each label is converted into an integer value. We will create a variable that contains the categories representing the education qualification of a person.

Python Code:

Fit and transform train data

df_train_transformed = encoder.fit_transform(train_df) One Hot Encoding

We use this categorical data encoding technique when the features are nominal(do not have any order). In one hot encoding, for each level of a categorical feature, we create a new variable. Each category is mapped with a binary variable containing either 0 or 1. Here, 0 represents the absence, and 1 represents the presence of that category.

These newly created binary features are known as Dummy variables. The number of dummy variables depends on the levels present in the categorical variable. This might sound complicated. Let us take an example to understand this better. Suppose we have a dataset with a category animal, having different animals like Dog, Cat, Sheep, Cow, Lion. Now we have to one-hot encode this data.

After encoding, in the second table, we have dummy variables each representing a category in the feature Animal. Now for each category that is present, we have 1 in the column of that category and 0 for the others. Let’s see how to implement a one-hot encoding in python.

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':[ 'Delhi','Mumbai','Hydrabad','Chennai','Bangalore','Delhi','Hydrabad','Bangalore','Delhi' ]}) #Create object for one-hot encoding encoder=ce.OneHotEncoder(cols='City',handle_unknown='return_nan',return_df=True,use_cat_names=True) #Original Data data #Fit and transform Data data_encoded = encoder.fit_transform(data) data_encoded

Now let’s move to another very interesting and widely used encoding technique i.e Dummy encoding.

Dummy Encoding

Dummy coding scheme is similar to one-hot encoding. This categorical data encoding method transforms the categorical variable into a set of binary variables (also known as dummy variables). In the case of one-hot encoding, for N categories in a variable, it uses N binary variables. The dummy encoding is a small improvement over one-hot-encoding. Dummy encoding uses N-1 features to represent N labels/categories.

To understand this better let’s see the image below. Here we are coding the same data using both one-hot encoding and dummy encoding techniques. While one-hot uses 3 variables to represent the data whereas dummy encoding uses 2 variables to code 3 categories.

Let us implement it in python.

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi,'Hyderabad']}) #Original Data data #encode the data data_encoded=pd.get_dummies(data=data,drop_first=True) data_encoded

Here using drop_first  argument, we are representing the first label Bangalore using 0.

Drawbacks of  One-Hot and Dummy Encoding

One hot encoder and dummy encoder are two powerful and effective encoding schemes. They are also very popular among the data scientists, But may not be as effective when-

A large number of levels are present in data. If there are multiple categories in a feature variable in such a case we need a similar number of dummy variables to encode the data. For example, a column with 30 different values will require 30 new variables for coding.

If we have multiple categorical features in the dataset similar situation will occur and again we will end to have several binary features each representing the categorical feature and their multiple categories e.g a dataset having 10 or more categorical columns.

In both the above cases, these two encoding schemes introduce sparsity in the dataset i.e several columns having 0s and a few of them having 1s. In other words, it creates multiple dummy features in the dataset without adding much information.

Also, they might lead to a Dummy variable trap. It is a phenomenon where features are highly correlated. That means using the other variables, we can easily predict the value of a variable.

Due to the massive increase in the dataset, coding slows down the learning of the model along with deteriorating the overall performance that ultimately makes the model computationally expensive. Further, while using tree-based models these encodings are not an optimum choice.

Effect Encoding:

This encoding technique is also known as Deviation Encoding or Sum Encoding. Effect encoding is almost similar to dummy encoding, with a little difference. In dummy coding, we use 0 and 1 to represent the data but in effect encoding, we use three values i.e. 1,0, and -1.

The row containing only 0s in dummy encoding is encoded as -1 in effect encoding.  In the dummy encoding example, the city Bangalore at index 4  was encoded as 0000. Whereas in effect encoding it is represented by -1-1-1-1.

Let us see how we implement it in python-

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi,'Hyderabad']}) encoder=ce.sum_coding.SumEncoder(cols='City',verbose=False,) #Original Data data encoder.fit_transform(data) Hash Encoder

To understand Hash encoding it is necessary to know about hashing. Hashing is the transformation of arbitrary size input in the form of a fixed-size value. We use hashing algorithms to perform hashing operations i.e to generate the hash value of an input. Further, hashing is a one-way process, in other words, one can not generate original input from the hash representation.

Hashing has several applications like data retrieval, checking data corruption, and in data encryption also. We have multiple hash functions available for example Message Digest (MD, MD2, MD5), Secure Hash Function (SHA0, SHA1, SHA2), and many more.

Just like one-hot encoding, the Hash encoder represents categorical features using the new dimensions. Here, the user can fix the number of dimensions after transformation using n_component argument. Here is what I mean – A feature with 5 categories can be represented using N new features similarly, a feature with 100 categories can also be transformed using N new features. Doesn’t this sound amazing?

By default, the Hashing encoder uses the md5 hashing algorithm but a user can pass any algorithm of his choice. If you want to explore the md5 algorithm, I suggest this paper.

import category_encoders as ce import pandas as pd #Create the dataframe data=pd.DataFrame({'Month':['January','April','March','April','Februay','June','July','June','September']}) #Create object for hash encoder encoder=ce.HashingEncoder(cols='Month',n_components=6) #Fit and Transform Data encoder.fit_transform(data)

Since Hashing transforms the data in lesser dimensions, it may lead to loss of information. Another issue faced by hashing encoder is the collision. Since here, a large number of features are depicted into lesser dimensions, hence multiple values can be represented by the same hash value, this is known as a collision.

Moreover, hashing encoders have been very successful in some Kaggle competitions. It is great to try if the dataset has high cardinality features.

Binary Encoding

Binary encoding is a combination of Hash encoding and one-hot encoding. In this encoding scheme, the categorical feature is first converted into numerical using an ordinal encoder. Then the numbers are transformed in the binary number. After that binary value is split into different columns.

Binary encoding works really well when there are a high number of categories. For example the cities in a country where a company supplies its products.

#Import the libraries import category_encoders as ce import pandas as pd #Create the Dataframe data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi','Hyderabad','Mumbai','Agra']}) #Create object for binary encoding encoder= ce.BinaryEncoder(cols=['city'],return_df=True) #Original Data data #Fit and Transform Data data_encoded=encoder.fit_transform(data) data_encoded

Binary encoding is a memory-efficient encoding scheme as it uses fewer features than one-hot encoding. Further, It reduces the curse of dimensionality for data with high cardinality.

Base N Encoding

Before diving into BaseN encoding let’s first try to understand what is Base here?

In the numeral system, the Base or the radix is the number of digits or a combination of digits and letters used to represent the numbers. The most common base we use in our life is 10  or decimal system as here we use 10 unique digits i.e 0 to 9 to represent all the numbers. Another widely used system is binary i.e. the base is 2. It uses 0 and 1 i.e 2 digits to express all the numbers.

For Binary encoding, the Base is 2 which means it converts the numerical values of a category into its respective Binary form. If you want to change the Base of encoding scheme you may use Base N encoder. In the case when categories are more and binary encoding is not able to handle the dimensionality then we can use a larger base such as 4 or 8.

#Import the libraries import category_encoders as ce import pandas as pd #Create the dataframe data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi','Hyderabad','Mumbai','Agra']}) #Create an object for Base N Encoding encoder= ce.BaseNEncoder(cols=['city'],return_df=True,base=5) #Original Data data

#Fit and Transform Data data_encoded=encoder.fit_transform(data) data_encoded

In the above example, I have used base 5 also known as the Quinary system. It is similar to the example of Binary encoding. While Binary encoding represents the same data by 4 new features the BaseN encoding uses only 3 new variables.

Hence BaseN encoding technique further reduces the number of features required to efficiently represent the data and improving memory usage. The default Base for Base N is 2 which is equivalent to Binary Encoding.

Target Encoding

Target encoding is a Baysian encoding technique.

Bayesian encoders use information from dependent/target variables to encode the categorical data.

In target encoding, we calculate the mean of the target variable for each category and replace the category variable with the mean value. In the case of the categorical target variables, the posterior probability of the target replaces each category..

#import the libraries import pandas as pd import category_encoders as ce #Create the Dataframe data=pd.DataFrame({'class':['A,','B','C','B','C','A','A','A'],'Marks':[50,30,70,80,45,97,80,68]}) #Create target encoding object encoder=ce.TargetEncoder(cols='class') #Original Data Data

#Fit and Transform Train Data encoder.fit_transform(data['class'],data['Marks'])

We perform Target encoding for train data only and code the test data using results obtained from the training dataset. Although, a very efficient coding system, it has the following issues responsible for deteriorating the model performance-

It can lead to target leakage or overfitting. To address overfitting we can use different techniques.

In the leave one out encoding, the current target value is reduced from the overall mean of the target to avoid leakage.

In another method, we may introduce some Gaussian noise in the target statistics. The value of this noise is hyperparameter to the model.

The second issue, we may face is the improper distribution of categories in train and test data. In such a case, the categories may assume extreme values. Therefore the target means for the category are mixed with the marginal mean of the target.

Frequently Asked Questions

To summarize, encoding categorical data is an unavoidable part of the feature engineering. It is more important to know what coding scheme should we use. Having into consideration the dataset we are working with and the model we are going to use. In this article, we have seen various encoding techniques along with their issues and suitable use cases.

If you want to know more about dealing with categorical variables, please refer to this article-


What Is Web3? What All You Need To Know About Web3 Technology?

What is web3? What do you need to know about web3 technology? Its features and layers

Web 3 or Web 3.0 has the potential to be disruptive and usher in a significant paradigm shift like Web 2.0 did. Web 3 is formed due to the fundamental ideas of decentralization, increased consumer usefulness, and openness. Web 3 technology plays the next step in the development of the internet.

Web 3 accurately translates and understands what you type through text, voice, or other media. The technology also understands what you say. In this article, we have discussed what is web3 and what all you need to know about web3 including its features of web3. Read to know about web3 technology.

What Is Web 3.0 Technology?

Web 3.0 or Web 3 is a third-generation world wide web built on top of blockchain developments and technologies in the Semantic Web. Web 3 is meant to be decentralized, and open to everyone which describes the web as a network of meaningfully linked data.

Key Features of Web3

Web3 has several distinguished features.

Decentralization: In web 2.0 computers search for the data that is kept at a fixed location mostly in a single server using HTTP in form of a web address. Information could be stored in multiple locations at the same time and become decentralized with Web 3.0 because it would be found based on its content rather than a single location. This would give people more power by destroying the massive databases that internet behemoths like Meta and Google currently maintain.

With the help of web 3, users will be able to sell their data through decentralized data networks, ensuring that they retain control of their ownership. This information will be generated by a wide range of powerful computing resources, including mobile phones, desktop computers, appliances, automobiles, and sensors.

Decentralization and open-source software-based Web 3.0 will also be trustless (i.e., participants will be able to interact directly without going through a trusted intermediary) and permissionless (each individual will be able to access without the permission of any governing body). This means that Web 3.0 applications, also known as dApps, will run on blockchains, decentralized peer-to-peer networks, or a hybrid of the two. DApps are decentralized apps.

Connectivity and ubiquity: With Web 3.0, content and information are more accessible across applications and an increasing number of commonplace internet-connected devices.

How Does Web 3 Work?

Your data is saved in web3 on your cryptocurrency notecase. On web3, you’ll interact with apps and communities via your wallet, and when you log out, your data will follow you. Because you own the data, you can theoretically decide whether to monetize it.

After we’ve established our guiding principles, we can look at how specific web3 development features are supposed to achieve these goals.

Data Ownership:

When you use a platform such as Facebook or YouTube, these companies collect, own, and recoup your data. Your information is saved in web3 on your cryptocurrency wallet. On web3, you’ll interact with apps and communities via your wallet, and when you log out, your data will follow you. Because you own the data, you can theoretically decide whether to monetize it.


Privacy, like data ownership, is a feature of your wallet. On web3, your wallet serves as your identification, making it difficult to link it to your actual identity. As a result, even if someone observes wallet activity, they will not be able to identify your wallet.

Some services of web 3 assist customers in connecting to cryptocurrency wallets used for illegal activity.

Although wallets improve the level of privacy for bitcoin transactions, privacy coins such as Zcash and Monero provide complete anonymity. Observers can track transactions on blockchains for privacy coins, but they cannot see the wallets involved.

Update the detailed information about Want To Become A Data Manager? Here Is All You Need To Know! on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!